StubHub is looking for a Staff Machine Learning Infrastructure Engineer to be a high-impact individual contributor focused on machine learning infrastructure and real-time inference systems. You will architect and build foundational ML platforms that power our recommendation systems, pricing optimization, and personalization.
What You'll Do
- Set technical direction and architect ML infrastructure strategy aligning technical approaches across Data Science, ML Engineering, and Platform teams.
- Build production-grade inference services with sub-100ms latency, intelligent caching, and 99.9% uptime SLAs.
- Design model lifecycle management systems including versioning, A/B testing, rollback capabilities, and performance monitoring.
- Modernize recommendation systems from legacy architecture to scalable, real-time streaming solutions.
- Prototype and investigate ambiguous, high-impact ML infrastructure problems.
- Lead complex technical initiatives spanning multiple teams and quarters.
- Mentor engineers across the platform and actively sponsor promising team members.
- Connect different team efforts to ensure ML infrastructure initiatives succeed and expedite high-priority ML infrastructure needs.
What We're Looking For
- 8+ years of relevant software or data engineering development experience in a fast-paced, high growth environment.
- 3+ years of experience with machine learning infrastructure, MLOps, or ML platform engineering.
- Proven track record of setting technical direction and leading complex, multi-team initiatives.
- Strong programming and analytical ability with expertise in Python, Scala, or Java, and infrastructure-as-code.
- Experience with feature store services and how they interoperate between batch and live inference systems.
- Experience with live inference services including caching, SLAs, and performance optimization for production ML workloads.
- Experience with model lifecycle management including versioning, A/B testing, and rollback capabilities.
- Experience with streaming systems (Spark, Kafka) and how they relate to overarching ML platform architecture.
- Experience with cloud-based ML platforms such as AWS SageMaker, Google Vertex AI, or Azure ML.
- Experience mentoring engineers and establishing technical best practices across teams.
- Technical leadership through influence rather than formal management authority.
- Strategic thinking with ability to balance long-term technical vision with immediate organizational needs.
- Cross-functional collaboration skills to work effectively with Data Science, Product, and Engineering teams.
- Communication skills to inject technical context into high-level organizational discussions.
- Problem-solving approach for ambiguous, high-impact technical challenges.
Nice to Have
- Experience with real-time recommendation systems and personalization platforms at scale.
- Knowledge of ML model serving frameworks (TensorFlow Serving, TorchServe, Seldon, etc.).
- Experience with A/B testing frameworks and experimentation platforms.
- Experience with distributed computing frameworks (Ray, Dask, etc.).
- Knowledge of ML security and privacy considerations.
- Track record of technical writing or speaking at conferences about ML infrastructure.
Technical Stack
- Python, Scala, Java
- AWS SageMaker, Google Vertex AI, Azure ML
- Spark, Kafka
Team & Environment
Part of the Data Engineering & Analytics team.
Benefits & Compensation
- Top Tier Compensation Package including stock incentives.
- Salary: $300,000 - $350,000 USD + equity.
- Flexible Time Off with unlimited Flex Time Off.
- Comprehensive benefits package featuring 401k, and premium Health, Vision, and Dental Insurance options.
- Accelerated Growth Environment with autonomy to lead experiments and tests on a massive scale.
- Paid parental leave.
Work Mode
This is a hybrid position based in New York, NY or Los Angeles, CA.
We are an equal opportunity employer and value diversity on our team. We do not discriminate on the basis of race, color, religion, sex, national origin, gender, sexual orientation, age, disability, veteran status, or any other legally protected status.
